System and Method for Combined Raman and LIBS Detection with Targeting

ABSTRACT

A system and method for locating and identifying unknown samples. A targeting mode may be utilized to scan regions of interest for potential unknown materials. This targeting mode may interrogate regions of interest using SWIR and/or fluorescence spectroscopic and imaging techniques. Unknown samples detected in regions of interest may be further interrogated using a combination of Raman and LIBS techniques to identify the unknown samples. Structured illumination may be used to interrogate an unknown sample. Data sets generated during interrogation may be compared to a reference database comprising a plurality of reference data sets, each associated with a known material. The system and method may be used to identify a variety of materials including: biological, chemical, explosive, hazardous, concealment, and non-hazardous materials.

RELATED APPLICATIONS

This application is a continuation-in-part of pending U.S. patentapplication Ser. No. 11/656,393, filed on Jan. 23, 2007, entitled“Method and System for Combined Raman and LIBS Detection,” which itselfclaims priority under 36 USC §119(e) to the following provisional patentapplications: No. 60/761,235, filed on Jan. 23, 2006, entitled “CombinedRaman And LIBS Biochem Detection System”; No. 60/761,256, filed on Jan.23, 2006, entitled “Raman Detection Of Waterborne Threats”; and No.60/761,235, filed on Jan. 23, 2006, entitled “Combined Raman And LIBSBiochem Detection System.”

This application is also a continuation-in-part of pending U.S. patentapplication Ser. No. 12/899,055, filed on Oct. 6, 2010, entitled “SystemAnd Method For Combined Raman And LIBS Detection,” which itself is acontinuation-in-part of pending U.S. patent application Ser. No.11/656,393, filed on Jan. 23, 2007, entitled “Method and System forCombined Raman and LIBS Detection.” Application Ser. No. 12/899,055 alsoclaims priority to Provisional Patent Application No. 61/278,393, filedon Oct. 6, 2009, entitled “Use Of Magnification To Increase SWIR HSIDetection Sensitivity.”

This application is also a continuation-in-part of pending U.S. patentapplication Ser. No. 12/899,119, filed on Oct. 6, 2010, entitled “Systemand Method for Combined Raman, SWIR and LIBS Detection,” which itself isa continuation in part of U.S. patent application Ser. No. 12/199,145,filed on Aug. 27, 2008, entitled, “Time And Space Resolved StandoffHyperspectral IED Explosives LIDAR Detector” and claims priority under§119(e) to the following U.S. Provisional Patent Applications: No.61/403,141, filed on Sep. 10, 2010, entitled “Systems And Methods ForImproving Imaging Technology”; No. 61/324,963, filed on Apr. 16, 2010,entitled “Short-Wavelength Infrared (SWIR) Multi-Conjugate LiquidCrystal Tunable Filter”; No. 61/305,667, filed on Feb. 18, 2010,entitled “System and Method for Detecting Explosives on Shoes andClothing”; No. 61/301,814, filed on Feb. 5, 2010, entitled “System andMethod for Detecting Hazardous Agents including Explosives”; No.61/335,785, filed on Jan. 12, 2010, entitled; and No. 61/278,393, filedon Oct. 6, 2009, entitled “System and method for SWIR HSI for daytimeand nighttime operations.”

This Application is also a continuation-in-part of pending U.S. patentapplication Ser. No. 12/199,145, filed on Aug. 27, 2008, entitled “TimeAnd Space Resolved Standoff Hyperspectral IED Explosives LIDARDetector,” which itself is a continuation of U.S. Pat. No. 7,692,775,filed on Jun. 9, 2006, entitled “Time And Space Resolved StandoffHyperspectral IED Explosives LIDAR Detection”, and claims priority under§119(e) to the following provisional patent applications: No.60/786,978, filed on Mar. 29, 2006, entitled “Time and Space ResolvedStandoff Hyperspectral IED Explosives LIDAR Detection (TSR-SHIELD)”; andNo. 60/699,251, filed on Jul. 14, 2005, entitled “SHIELD: StandoffHyperspectral Imaging Explosives LIDAR Detector/Optical StandoffDetection Of Explosive Residue.”

Each of the above-referenced patents and patents applications are herebyincorporated by reference in their entireties.

BACKGROUND

Deployment of threat agents poses significant risks to both human andeconomic health. The risk is compounded by a limited ability to detectthe deployment of these agents. Prior art detection strategies rely onseparate instrumentation for detection and identification of the threatagent. Conventional means to identify a threat agent include wetchemical methods or spectroscopic methods. Reagent-based identificationof biological threat agents includes methods such as specificantibodies, genetic markers and propagation in culture. While highlyspecific, these identification methods are time-consuming,labor-intensive and costly.

Spectroscopic means, for identification, provide an alternative toreagent-based identification methods and include mass spectrometry,infrared spectroscopy, Raman spectroscopy, laser induced breakdownspectroscopy (LIBS), and imaging spectrometry. Mass spectrometry islimited by sensitivity to background interference. Infrared spectroscopyholds potential for quickly scanning a region of interest to identifyunknown materials. Raman spectroscopy is a good candidate for detectionof threat agents based on its ability to provide a molecular“fingerprint” for materials. With high specificity, Raman spectroscopycan be implemented in several different configurations, including normalRaman spectroscopy, UV resonance Raman spectroscopy, surface enhancedRaman spectroscopy (SERS) and non-linear Raman spectroscopy.

Spectroscopic imaging combines digital imaging and molecularspectroscopy techniques, which can include Raman scattering,fluorescence, photoluminescence, ultraviolet, visible and infraredabsorption spectroscopies. When applied to the chemical analysis ofmaterials, spectroscopic imaging is commonly referred to as chemicalimaging. Instruments for performing spectroscopic (i.e. chemical)imaging typically comprise an illumination source, image gatheringoptics, focal plane array imaging detectors and imaging spectrometers.

In general, the sample size determines the choice of image gatheringoptic. For example, a microscope is typically employed for the analysisof sub micron to millimeter spatial dimension samples. For largerobjects, in the range of millimeter to meter dimensions, macro lensoptics are appropriate. For samples located within relativelyinaccessible environments, flexible fiberscope or rigid borescopes canbe employed. For very large scale objects, such as planetary objects,telescopes are appropriate image gathering optics.

For detection of images formed by the various optical systems,two-dimensional, imaging focal plane array (FPA) detectors are typicallyemployed. The choice of FPA detector is governed by the spectroscopictechnique employed to characterize the sample of interest. For example,silicon (Si) charge-coupled device (CCD) detectors or CMOS detectors aretypically employed with visible wavelength fluorescence and Ramanspectroscopic imaging systems, while indium gallium arsenide (InGaAs)FPA detectors are typically employed with near-infrared spectroscopicimaging systems.

Spectroscopic imaging of a sample can be implemented by one of twomethods. First, a point-source illumination can be provided on thesample to measure the spectra at each point of the illuminated area.Second, spectra can be collected over the entire area encompassing thesample simultaneously using an electronically tunable optical imagingfilter such as an acousto-optic tunable filter (“AOTF”) or a LCTF. Thismay be referred to as “wide-field imaging”. Here, the organic materialin such optical filters are actively aligned by applied voltages toproduce the desired bandpass and transmission function. The spectraobtained for each pixel of such an image thereby forms a complex dataset referred to as a hyperspectral image (“HSI”) which contains theintensity values at numerous wavelengths or the wavelength dependence ofeach pixel element in this image.

Spectroscopic devices operate over a range of wavelengths due to theoperation ranges of the detectors or tunable filters possible. Thisenables analysis in the Ultraviolet (“UV”), visible (“VIS”), nearinfrared (“NIR”), short-wave infrared (“SWIR”), mid infrared (“MIR”)wavelengths and to some overlapping ranges. These correspond towavelengths of about 180-380 nm (UV), 380-700 nm (VIS), 700-2500 nm(NIR), 900-1700 nm (SWIR), and 2500-25000 nm (MIR).

While normal Raman spectroscopy has demonstrated adequate sensitivityand specificity for detection of airborne matter, other forms of Ramanspectroscopy suffer from inadequate sensitivity, specificity orsignature robustness. LIBS is also a good candidate for detection ofthreat agents based on its ability provide an elemental “fingerprint”for materials with high sensitivity. Prior art imaging spectroscopy islimited by the need to switch from a broadband light source, for opticalimaging, to a substantially monochromatic light source for spectroscopicimaging. This results in delay and inefficiency during detection duringwhich the sample may degrade.

There exists a need for accurate and reliable detection of a variety ofagents including but not limited to biological, chemical, explosive,hazardous, concealment, and non-hazardous. There exists a need for asystem and method that can detect and unknown material in a region ofinterest and interrogate the unknown material to thereby identify it asa known material.

SUMMARY OF THE INVENTION

The present disclosure relates generally to the identification ofunknown samples. More specifically the present disclosure relates tosystems and methods for targeting and identifying an unknown sample in aregion of interest. The system and method of the present disclosure alsoprovide for operation in at least two modalities. First, the presentdisclosure contemplates a targeting mode, for scanning a region ofinterest to thereby identify an unknown sample. Short wave infrared(SWIR) and fluorescence techniques may be implemented in this targetingmode. The present disclosure then contemplates an identification mode,for interrogating and indentifying the unknown sample.

In order to improve the overall sensitivity and specificity of fieldablethreat detection, the invention of the present disclosure combines twowell known and proven techniques in this identification mode, Raman andlaser induced breakdown spectroscopy (LIBS), into a system optimized forthreat detection. Both individual methods have demonstrated the abilityto detect threats in point sensing, proximity sensing and standoffsensing configurations. Improved overall detection performance can berealized through appropriate chemometric spectral processing algorithmsapplied to the fused data of the two orthogonal techniques. By combiningRaman and LIBS techniques, threat detection performance can be improvedrelative to the individual techniques acting alone.

The present disclosure contemplates that the systems and methodsdisclosed herein may be use to identify materials including, but notlimited to, biological materials, chemical materials, explosivematerials, hazardous materials, concealment materials, non-hazardousmaterials, and combinations thereof. The system and method disclosedherein hold potential for application in a variety of configurationsincluding standoff, on-the-move (OTM), stationary, portable, handheld,and combinations thereof.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other embodiments of the disclosure will be discussed inrelation to the following non-limiting and exemplary drawings, in which:

FIG. 1 is a spectroscopy system according to one embodiment of thedisclosure;

FIG. 2A is an exemplary structured illumination configuration accordingto one embodiment of the disclosure;

FIG. 2B is another exemplary structured illumination configurationaccording to one embodiment of the disclosure;

FIG. 2C is yet another exemplary structured illumination configurationaccording to one embodiment of the disclosure;

FIG. 3 is a schematic representation for an apparatus according to oneembodiment of the disclosure;

FIGS. 4A and 4B respectively show LIBS and Raman spectra of a sample;and

FIG. 5 is illustrative of a method of the present disclosure.

FIG. 6 is illustrative of a method of the present disclosure.

FIG. 7 is illustrative of a system of the present disclosure.

DETAILED DESCRIPTION

Raman spectroscopy has emerged as an attractive candidate forreagentless detection technology and shows significant capabilities incontrolled studies for field detection of a variety of agents including:chemical, Radiological, nuclear, and explosive (CBRNE) biologicalagents. Specifically, Raman sensing may hold potential for detection ofchemical surface contamination, on-the-move detection, white powderidentification using handheld Raman sensors, and for waterborne pathogendetection.

Laser Induced Breakdown Spectroscopy (LIBS) is a type of atomic emissionspectroscopy which uses a highly energetic laser pulse as the excitationsource. Because all substances emit light when excited to sufficientlyhigh temperatures, LIBS can detect all elements, limited only by thepower of the laser as well as the sensitivity and wavelength range ofthe spectrograph and the detector. The development of the broadband,high-resolution spectrometer, along with advanced chemometricapproaches, has enabled LIBS to demonstrate real-time detection anddiscrimination of hazardous chemical, biological and explosive (CBRNE)materials. Operationally, LIBS is very similar to arc/spark emissionspectroscopy. The laser pulses delivered to the sample can be mildlydestructive of the sample. However, the laser pulses can be directed toa specific region of the sample, making the surrounding sample materialavailable for Raman sampling.

Thus, according to one embodiment of the disclosure an integrateddetection system synergistically combines Raman detection mode with LIBStechnologies to provide an integrated and efficient detection system.The combined Raman/LIBS sensory system can provide reagentless sensingtechnology for the detection and identification of chemical orbiological agents. In another embodiment, the disclosure relates to astructured illumination method and apparatus.

FIG. 1 is a spectroscopy system according to one embodiment of thedisclosure. The system shown in FIG. 1 can be configured as a handhelddevice, point detection device, or a standoff detector device. Thespectroscopy device of FIG. 1 can be used, for example, tosimultaneously obtain spectroscopic images of a sample. The images candefine different spectroscopic modes such as laser scattering,ultraviolet laser induced fluorescence (UV-LIF) and laser inducedbreakdown spectroscopy (LIBS). In FIG. 1, illumination source 110provides a plurality of illuminating photons to sample 115. Opticaldevice 114 may include one or more light gathering optics and it mayoptionally be used to focus, filter or direct illumination photons 112to sample 115. Once illuminated, sample photons 122 can be collected bygathering optics 130 and directed to spectrometer 140. Spectrometer 140can be configured to receive and process different types of spectrasimultaneously. In one embodiment, spectrometer 140 receives andprocesses sample photons for simultaneously forming Raman and LIBSspectra for sample 115. In one embodiment, first sample photons areprocessed to obtain Raman spectra for the sample and then second samplephotons are processed to obtain LIBS spectra for the sample.

The exemplary system of FIG. 1 can include a fiber array spectraltranslator (“FAST”). The FAST system can provide faster real-timeanalysis for rapid detection, classification, identification, andvisualization of, for example, explosive materials, hazardous agents,biological warfare agents, chemical warfare agents, and pathogenicmicroorganisms, as well as non-threatening objects, elements, andcompounds. FAST technology can acquire a few to thousands of fullspectral range, spatially resolved spectra simultaneously, This may bedone by focusing a spectroscopic image onto a two-dimensional array ofoptical fibers that are drawn into a one-dimensional distal array with,for example, serpentine ordering. The one-dimensional fiber stack iscoupled to an imaging spectrograph. Software may be used to extract thespectral/spatial information that is embedded in a single CCD imageframe.

One of the fundamental advantages of this method over otherspectroscopic methods is speed of analysis. A complete spectroscopicimaging data set can be acquired in the amount of time it takes togenerate a single spectrum from a given material. FAST can beimplemented with multiple detectors. Color-coded FAST spectroscopicimages can be superimposed on other high-spatial resolution gray-scaleimages to provide significant insight into the morphology and chemistryof the sample.

The FAST system allows for massively parallel acquisition offull-spectral images. A FAST fiber bundle may feed optical informationfrom is two-dimensional non-linear imaging end (which can be in anynon-linear configuration, e.g., circular, square, rectangular, etc.) toits one-dimensional linear distal end. The distal end feeds the opticalinformation into associated detector rows. The detector may be a CCDdetector having a fixed number of rows with each row having apredetermined number of pixels. For example, in a 1024-width squaredetector, there will be 1024 pixels (related to, for example, 1024spectral wavelengths) per each of the 1024 rows.

The construction of the FAST array requires knowledge of the position ofeach fiber at both the imaging end and the distal end of the array. Eachfiber collects light from a fixed position in the two-dimensional array(imaging end) and transmits this light onto a fixed position on thedetector (through that fiber's distal end).

Each fiber may span more than one detector row, allowing higherresolution than one pixel per fiber in the reconstructed image. In fact,this super-resolution, combined with interpolation between fiber pixels(i.e., pixels in the detector associated with the respective fiber),achieves much higher spatial resolution than is otherwise possible.Thus, spatial calibration may involve not only the knowledge of fibergeometry (i.e., fiber correspondence) at the imaging end and the distalend, but also the knowledge of which detector rows are associated with agiven fiber.

In one embodiment, the portable device may comprise FAST technologyavailable from ChemImage Corporation, Pittsburgh, PA. This technology ismore fully described in the following U.S. patents, hereby incorporatedby reference in their entireties: U.S. Pat. No. 7,764,371, filed on Feb.15, 2007, entitled “System And Method For Super Resolution Of A SampleIn A Fiber Array Spectral Translator System”; U.S. Pat. No. 7,440,096,filed on Mar. 3, 2006, entitled “Method And Apparatus For CompactSpectrometer For Fiber Array Spectral Translator”; U.S. Pat. No.7,474,395, filed on Feb. 13, 2007, entitled “System And Method For ImageReconstruction In A Fiber Array Spectral Translator System”; and U.S.Pat. No. 7,480,033, filed on Feb. 9, 2006, entitled “System And MethodFor The Deposition, Detection And Identification Of Threat Agents UsingA Fiber Array Spectral Translator”.

For example, transmission line 132 can comprise a fiber bundle such thata first end of the fiber bundle optically communicates with gatheringoptics 130 while the second end of the fiber bundle communicates withspectrometer 140. The first end of the fiber bundle can comprise a twodimensional non-linear array of fiber bundles. The second end of thefiber bundle can comprise a curvilinear array of fibers whereincurvilinear may include a straight line as well as a curved lineconfiguration. In an alternative embodiment, the system of FIG. 1 mayadditionally include an optical filter such as Liquid Crystal TunableFilter (LCTF), Multi-Conjugate Liquid Crystal Tunable Filter (MCF) or anAcousto-Optic Tunable Filter (AOTF). The system of FIG. 1 may also beconfigured for use with Computed Tomography Imaging Spectroscopy (CTIS).

FIG. 2A is an exemplary structured illumination configuration accordingto one embodiment of the disclosure. In FIG. 2A, illumination circle 200represents an illuminated area of a sample. Area 200 can be illuminatedwith photons having a first wavelength and region 210 can be illuminatedwith photons having a second wavelength. Thus, area 200 can beilluminated with photons of a first wavelength to obtain a Raman spectrafor area 200. Thereafter, region 210 can be illuminated with photons ofa second wavelength to obtain LIBS spectra for region 210. The samplecan be illuminated to obtain Raman spectra before LIBS. Alternatively,the sample can be illuminated to obtain LIBS spectra before Raman. Instill another embodiment, the annulus area between rings 200 and 210 canbe used to obtain LIBS spectra and region 210 can be used for obtainingRaman spectra.

In an embodiment, area 200 and region 210 can be illuminatedsimultaneously with photons of different wavelength. Photons of a firstwavelength can illuminate the entire area 200 (or the annulus regionbetween area 200 and region 210), and photons of a second wavelength canilluminate region 210. Raman spectra can be collected from regions220-270, while LIBS spectra are simultaneously collected from region210. In the event that the region 210 is illuminated simultaneously withphotons of the first and second wavelength, optical filters anddetectors can be used to remove unwanted sample photons.

In another embodiment of FIG. 2A, each of regions 220-270 shows a regionof the sample from which Raman-scattered photons may be collected.Region 210 can represent a region for which LIBS can be implemented toobtain an atomic signature of the sample under study. The atomicsignature of the sample can define the chemical identify of the sampleat region 210. Regions 210-270 can have the shape of a circle, anellipse, a rectangle, a square, a hexagon or any other shape. Thecombined analysis is advantageous in that it provides a significantsynergistic performance of Raman and LIBS. That is, the structuredillumination provides the specificity of Raman molecular spectroscopyalong with LIBS elemental spectroscopy.

The structured illumination configuration of FIG. 2A can reflect anarrangement of the illumination sources (not shown). For example, theillumination configuration can comprise a first laser source forilluminating the entire region with photons of a first frequency and asecond laser source for illuminating region 210 with photons of a secondfrequency. The arrangement of the first and second laser sources can beadapted to provide the structured illumination of FIGS. 2A-2C orvariations thereof.

As stated, area 200 and region 210 can be illuminated simultaneously orsequentially. In one embodiment, area 200 is first illuminated withphotons of the first wavelength. Sample photons can then be collectedfrom each of the regions 220-270. Next, region 210 can be illuminatedwith photons of a second wavelength and sample photons can be collectedtherefrom. In an embodiment where the first wavelength provides a Ramanspectrum and the second wavelength provides laser induced breakdownspectroscopy of the sample, collecting Raman photons from the samplebefore implementing laser induced breakdown spectroscopy enables Ramandetection before a region of the sample (e.g., region 210) may bepartially destroyed by LIBS.

In another embodiment, area 200 is illuminated substantiallysimultaneously with region 210. That is, photons of the first wavelengthand photons of the second wavelength are directed to the sample atsubstantially the same time to independently collect sample photons fromarea 200 and region 210. According to this embodiment, the detection andanalysis of the sample can be implemented simultaneously. Suchimplementation can be particularly beneficial for large samples where asample is divided into a number of segments and each segment is analyzedindependently of the remaining segments.

FIG. 2B is another exemplary structured illumination configurationaccording to an embodiment of the disclosure. In the structuredillumination configuration of FIG. 2B, the area 200 is illuminated withphotons of a first wavelength and region 270 can be illuminated withphotons of a second wavelength. The photons of the first wavelength canelicit Raman spectra for regions 210-260 while sample photons collectedfrom region 270 can identify the sample through LIBS. The illuminationof area 200 and region 270 can overlap. That is, both area 200 andregion 270 can be illuminated simultaneously.

Similarly, FIG. 2C is yet another exemplary structured illuminationconfiguration according to one embodiment of the disclosure. In FIG. 2C,area 200 is illuminated with photons having a first wavelength tocollect sample photons from regions 210, 220, 230, 240 and 270. Photonshaving a second wavelength illuminate different regions of the sample toprovide sample photons from regions 250 and 260. The sample photons fromdifferent regions 210-270 can be used to identify the sample. Forexample, if Raman spectra is collected from regions 210, 220, 230, 240and 270 and regions 250 and 260 are used for LIBS, the sample understudy can be identified by its Raman spectra and its atomic emission.

FIG. 3 is a schematic representation for an apparatus according to oneembodiment of the disclosure. FIG. 3 can provide illumination source aswell as the collection optics and the spectroscopy device. Morespecifically, FIG. 3 provides integrated handheld device 300 for sampledetection and analysis. Handheld device 300 can include illuminationsource 315 and collection point 316. The illumination source can beintegrated with the handheld device or it can be provided as a nozzleattachment. In one embodiment of the disclosure, nozzle 316 can beconfigured to collect sample photons. Further, the illumination sourcecan be configured to provide structured illumination for sample 320. InFIG. 3, sample 320 is illuminated with photons of a first wavelength atregion 310 and photons of a second wavelength at region 330. Regions 310and 330 can overlap as shown. Photons collected from region 310 canprovide laser induced breakdown spectroscopy and photons collected fromthe remainder of region 330 can be used to construct a Raman spectra forthe sample. Both regions 310 and 330 of sample 320 can be illuminatedsimultaneously by an illumination source configured to provide photonsof a first wavelength to region 330 and photons of a second wavelengthto region 310. The illumination source may comprise two laserillumination devices concentrically positioned to form an annulus and toprovide the illumination shown in FIG. 3.

FIG. 4A shows LIBS spectra collected from a sample. Specifically, FIG.4A shows the presence of Yersinia Rhodei (YR) 401, MS2 bacteriophagevirus 402, and bacillus globigii (BG) 403 as indicated by each of theirrespective spectra. FIG. 4B shows Raman spectra collected from thesample of FIG. 4A. The Raman spectrum for each of YR 411, MS2 virus 412.and BG 413 are shown. In addition, at the bottom of FIGS. 4A and 4B,confusion matrices are shown for each of the Raman, LIBS and combinedRaman/LIBS sensing, respectively, of YR, MS2 and

BG.

A confusion matrix quantities the degree or relatedness of spectrawithin specific classes contained in a training dataset, as well asproviding an estimate of the degree of specificity inherent in theanalysis and dominant sources of interference between classes(crosstalk). In this example, the classes are comprised of Yr, MS2 andBG. The confusion matrix is calculated by organizing the species-levelRaman spectra into three unique classes. PCA analysis was performed andthe first 10 PCs were employed to construct a supervised Mahalanobisdistance model boundary classifier for each of the 3 biological classes.The classifier consisted of a mean. spectrum, covariance matrix, and anellipsoidal boundary. Each spectrum, as a point in the N=10 dimensionalPC dataspace, was compared with the ellipsoidal boundaries. The minimumdistance classification rule (nearest neighbor approach) was usedwhereby a spectrum was deemed a member of a particular class(ellipsoidal boundary) if its distance from that class was less than itsdistance from all other classes. Each row in the confusion matrix is thebiological identity of the spectra, and the column entries show how theMahalanobis distance based classifier classified the spectra. A perfectclassifier has entries only along the diagonal. Confusion matrices are apredictor of the specificity of an identification algorithm in which thediagonal elements are correlated with the probabilities of correctidentification (P.sub.d) for each of the species, while the off-diagonalelements correlate with the probability of false positive (P.sub.fp).The confusion matrix can change depending on the spectral range andnumber of principal components employed to construct the MD model. Inthe confusion matrices of FIGS. 4A and 4B, it is evident that there is areduction in probability of false positive detections in the Raman/LIBScombined approach relative to Raman or LIBS operating alone

FIG. 5 is an exemplary algorithm according to an embodiment of thedisclosure. The exemplary algorithm of FIG. 5 can define a software or afirmware. The exemplary algorithm of FIG. 5 can be used with the systemof FIG. 1 or apparatus of FIG. 3. In the optional step 510, the sampleis visually divided into several sections. For example, the sample canbe visually divided into a grid and each grid (section) can be analyzedindependently. In step 520, a selected section of the sample isilluminated with photons of a first wavelength to obtain a first samplephotons. The first sample photons can be used for Raman spectroscopy. Instep 530, the selected section is illuminated with photons of a secondwavelength to obtain second sample photons. The second sample photonscan be used for laser induced breakdown spectroscopy. Steps 520 and 530can be implemented substantially simultaneously or sequentially.

The first sample photons can be used to obtain the Raman spectra for thesample at step 540. The information can also be used to obtain aspatially accurate, wavelength resolved image of the section understudy. That is, the spatially accurate, wavelength resolved image of thesample can be obtained for the Raman spectra as well as the LIBSspectra. A spatially accurate wavelength-resolved image is an image of asample that is formed from multiple “frames” wherein each frame hasplural spatial dimensions and is created from photons of a particularwavelength (or wave number) or from photons in a particular wavelengthband (or wave number band) so that the frames may be combined to form acomplete image across all wavelengths (wave numbers) of interest. Thesecond sample photons can be used to obtain the atomic characteristic ofthe sample in step 550. The results from steps 540 and 550 can be usedto section of the sample under study. Steps 520-550 can be repeated tostudy different visual sections of the sample as shown by arrow 560.

In another embodiment, the disclosure relates to a method and apparatusfor detecting and identifying chemical or biological agents, includingaerosols and low vapor pressure chemicals by using electrostaticcollection devices with hyperspectral Raman imaging devices. Thedetection processes can be implemented without using reagents. Anexemplary system can include: (1) an electrostatic collector forparticulate collection and low vapor pressure chemical aerosolcollection; (2) an autonomous surface deposition subsystem providingconcentrated threat agents; (3) a hyperspectral Raman imaging sensoroptionally having a low-power imaging sensor, a digital camera forsample focusing and an imaging spectrometer for generatingspatially-resolved Raman spectra with sampling statistics necessary todifferentiate target from background; and (4) a decision makingalgorithm for threat agent identification in the presence of clutter orbackground noise.

In another embodiment, the disclosure relates to a reagentless detectorfor biological threats in water. Biological sample variables include:genetic near neighbors, strain, serotype, growth conditions andviability. To identify the substance, Mahalanobis Distance correlationmetric can be used. In a method according to one embodiment, detectionand identification of waterborne threats without using reagentscomprises the following process steps: sample collection; agentpre-concentration; detection and identification; automated decisionmaking; and data management. The agent pre-concentration step caninclude: sample collection, water-contaminant pre-concentration, andsample deposition. The detection and identification step can includeoptical microscopy as well as Raman spectroscopy and imaging. Theautomated decision making step may include one or more algorithm foranalyzing the spectroscopy results and identifying the sample.

The present disclosure provides for a method for targeting andidentifying an unknown material. One embodiment, the method, providesfor scanning a region of interest to thereby identify an unknown sample.This “scanning” may be referred to as operating in a targeting mode. Inone embodiment, this targeting mode may implement SWIR and/orfluorescence techniques to locate an unknown sample. This unknown samplemay then be further interrogated using Raman and LIBS techniques toidentify the unknown sample.

In one embodiment, illustrated by FIG. 6, the method 600 comprisesilluminating a region of interest in step 605 to thereby generate afirst plurality of interacted photons. Interacted photons as describedherein may refer to one or more of the following: scattered photons,reflected photons, absorbed photons, luminescence emitted photons,plasma emitted photons, transmitted photons, and combinations thereof.Interacted photons may be generated by illuminating a region of interestand/or an unknown sample.

This illumination may be accomplished using active illumination, passiveillumination, or combinations thereof. Active illumination may beaccomplished by configuring an active illumination source to illuminatethe region of interest. This active illumination source may comprise alaser light source, a broadband light source, an ambient light source,and combinations thereof. In one embodiment, a laser light source maycomprise a tunable laser. In another embodiment, passive illuminationmay be accomplished by configuring a passive illumination source toilluminate the region of interest. This passive illumination source maycomprise solar radiation.

In step 610 this, first plurality of interacted photons may be filtered.In one embodiment, this filtering may be achieved using a fixed filter,a dielectric filter, and combinations thereof. In another embodiment,this filtering may be achieved using a tunable filter. This tunablefilter may be selected from the group consisting of: a liquid crystaltunable filter, a multi-conjugate tunable filter, an acousto-opticaltunable filter, a Lyot liquid crystal tunable filter, an Evanssplit-element liquid crystal tunable filter, a Sole liquid crystaltunable filter, a ferroelectric liquid crystal tunable filter, a FabryPerot liquid crystal tunable filter, and combinations thereof.

In one embodiment, tunable filters described herein may comprise filtertechnology available from ChemImage Corporation, Pittsburgh, Pa. Thistechnology is more fully described in the following U.S. patents andpatent applications: U.S. Pat. No. 6,992,809, filed on Jan. 31, 2006,entitled “Multi-Conjugate Liquid Crystal Tunable Filter,” U.S. Pat. No.7,362,489, filed on Apr. 22, 2008, entitled “Multi-Conjugate LiquidCrystal Tunable Filter,” Ser. No. 13/066,428, filed on Apr. 14, 2011,entitled “Short wave infrared multi-conjugate liquid crystal tunablefilter.” These patents and patent applications are hereby incorporatedby reference in their entireties.

In step 615 this first plurality of interacted photons may be detectedto thereby generate a test data set representative of said region ofinterest. In one embodiment, this test data set may comprise a SWIR dataset representative of said region of interest. In one embodiment, thisSWIR data set may comprise at least one hyperspectral SWIR image. Inanother embodiment, this SWIR data set may comprise at least one of: aspatially accurate wavelength resolved SWIR image, a SWIR spectrum, andcombinations thereof.

In another embodiment, this test data set may comprise a fluorescencedata set representive of said region of interest. In one embodiment,this fluorescence data set may comprise at least one hyperspectralfluorescence image. In another embodiment, this fluorescence data setmay comprise at least one of: a spatially accurate wavelength resolvedfluoresce image, a fluorescence spectrum, and combinations thereof.

This test data set may be analyzed in step 620 to thereby identify anunknown sample within said region of interest. In one embodiment, themethod 600 may further comprise providing a reference databasecomprising a plurality of reference data sets, each reference data setcorresponding to a known sample. In one embodiment, the reference datasets may comprise at least one of: reference SWIR data sets, referencefluorescence data sets and combinations thereof. In one embodiment, thetest data set may be compared to at least one reference data set using achemometric technique, This chemometric technique may be selected fromthe group consisting of: principle component analysis (“PCA”), partialleast squares discriminate analysis (“PLSDA”), cosine correlationanalysis (“CCA”), Euclidian distance analysis (“EDA”), k-meansclustering, multivariate curve resolution (“MCR”), band t. entropymethod (“BTEM”), mahalanobis distance (“MD”), adaptive subspace detector(“ASD”), spectral mixture resolution, and combinations thereof. Inanother embodiment, pattern recognition algorithms may be used.

This unknown sample may be further interrogated in step 625 byilluminating a first portion of said unknown sample to thereby generatea second plurality of interacted photons. In one embodiment, the samplemay be illuminated using a first illumination pattern. This illuminationpattern may comprise at least one of: a circle, a square, a rectangle,an ellipse, an annulus, and combinations thereof.

In step 630 a second portion of said unknown sample may be illuminatedto thereby generate a third plurality of interacted photons. In oneembodiment, this second portion may be illuminated using a secondillumination pattern. This second illumination pattern may be selectedfrom the group consisting of: a circle, a square, a rectangle, anellipse, an annulus, and combinations thereof.

In one embodiment, at least one of a first portion of an unknown sampleand a second portion of a unknown sample may be illuminated usingpassive illumination, such as solar illumination. In another embodiment,at least one of a first portion of an unknown sample and a secondportion of an unknown sample may be illuminated using activeillumination such as a laser illumination source, a broad band lightsource, an ambient light source, and combinations thereof. In oneembodiment this laser illumination source may comprise a tunable laser.In yet another embodiment, a combination of active and passiveillumination may be implemented to illuminate a first portion of anunknown sample and a second portion of an unknown sample.

In one embodiment, a first portion and a second portion of an unknownsample may be illuminated sequentially. In another embodiment, a firstportion and a second portion of an unknown sample may be illuminatedsimultaneously. In one embodiment a first portion of said unknown sampleand a second portion of an unknown sample may be selected so as to atleast partially overlap.

At least one of said second plurality of interacted photons and saidthird plurality of interacted photons may be filtered in step 635. Inone embodiment, this filtering may be achieved using a fixed filter, adielectric filter, and combinations thereof. In another embodiment, thisfiltering may be achieved using a tunable filter. This tunable filtermay be selected from the group consisting of: a liquid crystal tunablefilter, a multi-conjugate tunable filter, an acousto-optical tunablefilter, a Lyot liquid crystal tunable filter, an Evans split-elementliquid crystal tunable filter, a Solc liquid crystal tunable filter, aferroelectric liquid crystal tunable filter, a Fabry Perot liquidcrystal tunable filter, and combinations thereof.

In step 640 the second plurality of interacted photons may be detectedto thereby generate a LIBS data set representative of said unknownsample. In one embodiment, this LIBS data set may comprise at least onehyperspectral LIBS image. In another embodiment, this LIBS data set maycomprise at least one of: a spatially accurate wavelength resolved LIBSimage, a LIBS spectrum, and combinations thereof.

In step 645 said third plurality of interacted photons may be detectedto thereby generate a Raman data set representative of said unknownsample. In one embodiment, this Raman data set may comprise at least onehyperspectral Raman image representative of said unknown sample. Inanother embodiment, this Raman data set may comprise at least one of: aspatially accurate wavelength resolved Raman image, a Raman spectrum,and combinations thereof.

At least one of said LIBS data set and said Raman data set may beanalyzed in step 650 to thereby identify said unknown sample. In oneembodiment, the unknown sample may be identified as comprising at leastone of: a biological material, a chemical material, an explosivematerial, a hazardous material, a concealment material, a non-hazardousmaterial, and combinations thereof. Explosive materials that may bedetected using the system and method disclosed herein include, but arenot limited to: nitrocellulose, Ammonium nitrate (“AN”), nitroglycerin,1,3,5-trinitroperhydro-1,3,5-triazine (“RDX”),3,5,7-tetranitroperhydro-2,3,5,7-tetrazocine (“HMX”) and1,3,-Dinitrato-2,2-bis(nitratomethyl) propane (“PETN”).

In one embodiment, this analyzing may comprise comparing at least one ofsaid LIBS data set and said Raman data set to at least one referencedata set. This comparing maybe achieved using a chemometric technique.In one embodiment, at least one of said LIBS data set and said Ramandata set may be applied to a plurality of reference data sets in areference database, wherein each said reference data set corresponds toa known sample.

In one embodiment, analyzing said LIBS data set and said Raman data setmay further comprise applying a fusion algorithm to thereby generate afused data set. In one embodiment, this fusion may be accomplished usingBayesian fusion. In another embodiment, this fusion may be accomplishedusing technology available from ChemImage Corporation, Pittsburgh, Pa.This technology is more fully described in the following pending U.S.patent applications: No. US200910163369, filed on Dec. 19, 2008 entitled“Detection of Pathogenic Microorganisms Using Fused Sensor Data,” Ser.No. 13/081,992, filed on Apr. 7, 2011, entitled “Detection of PathogenicMicroorganisms Using Fused Sensor Raman, SWIR and LIBS Sensor Data,” No,US2009/0012723, filed on Aug. 22, 2008, entitled “Adaptive Method forOutlier Detection and Spectral Library Augmentation,” No.US2007/0192035, filed on Jun. 9, 2006, “Forensic Integrated SearchTechnology,” and No. US2008/0300826, filed on Jan. 22, 2008, entitled“Forensic Integrated Search Technology With Instrument Weight FactorDetermination.” These applications are hereby incorporated by referencein their entireties. In another embodiment, the present disclosureprovides for ChemFusion Improvements. Such improvements include the useof grid search methodology to establish improved weighting parametersfor individual sensor modality classifiers under JFIST Bayesianarchitecture. In another embodiment, image weighted Bayesian fusion maybe used. This fused data set may then be analyzed to identify theunknown sample.

In another embodiment, the method of the present disclosure may providefor the time-gated detection of the photons reflected, scattered, and/orplasma emitted by the sample. In such an embodiment, an illuminationsource may be operatively coupled to one or more detection devices so asto acquire Raman, SWIR, and/or LIBS data in accordance with Raman, SWIR,and/or LIBS emission times. The use of pulsed laser excitation andtime-gated detection is more fully described in U.S. patent applicationSer. No. 12/802,994, filed on Jun. 17, 2010, which is herebyincorporated by reference in its entirety.

The methods of the present disclosure may further utilize telescopeoptics and/or zoom lenses to thereby locate and/or focus on a region ofinterest and/or unknown sample. The telescope optics may also beutilized to collect at least one of the plurality of interacted photonsgenerated by illuminating at least one of a region of interest and anunknown sample.

The use of LWIR spectroscopy and imaging techniques may be used, in oneembodiment, to detect human presence in a scene and human movement in ascene. This use of LWIR may be used in conjunction with motion sensingto thereby configure laser interlocking. This effectively turns off alaser when a human is present. This holds potential for increasingsafety, including eye safety, of the system and method disclosed herein.

The present disclosure also provides for a system for the detection andidentification of explosive and other materials. In one embodiment, asystem may comprise a first illumination source configured so as toilluminate a region of internet to thereby generate a first plurality ofinteracted photons. In one embodiment, this first illumination sourcemay comprise an active illumination source such as a laser illuminationsource, a broadband light source, an ambient light source, andcombinations thereof. In one embodiment, this laser illumination sourcemay comprise a tunable laser. In another embodiment, this firstillumination source may comprise a passive illumination source such as asolar radiation source.

The system may further comprise at least one collection opticsconfigured so as to collect said first plurality of interacted photons.This collection optics may, in one embodiment, comprise a telescopeoptics. A filter may be configured so as to filter the first pluralityof interacted photons. This filter may comprise a tunable filter, afixed filter, a dielectric filter, and combinations thereof. In aconfiguration comprising a tunable laser illumination source, a fixedfilter may be implemented in the system.

A first detector may be configured so as to detect said first pluralityof interacted photons and generate a test data set representative ofsaid region of interest. In one embodiment, this detector may comprise afocal plane array detector. This focal plane array detector may compriseat least one of: an InGaAs detector, a CMOS detector, an ICCD detector,a CCD detector, and combinations thereof. In one embodiment, this firstdetector may be configured so as to generate a SWIR test data setrepresentative of said region of interest. In another embodiment, thisfirst detector may be configured so as to generate a fluorescence testdata set representative of said region of interest.

A means for analyzing said test data may include software configured soas to compare the test data set to reference data sets in a referencelibrary. This comparing may be achieved by applying a chemometrictechnique. This analysis may target an unknown material in the region ofinterest.

A second illumination source may be configured so as to illuminate atleast a portion of the unknown material to thereby generate a secondplurality of interacted photons. This second illumination source maycomprise at least one of: a passive illumination source, a laserillumination source, a broadband light source, an ambient light source,and combinations thereof. A second detector may be configured so as todetect this second plurality of interacted photons and generate a LIBSdata set representative of the unknown material. A third illuminationsource may be configured to illuminate a second portion of the unknownsample to thereby generate a third plurality of interacted photons. Thissecond illumination source may comprise at least one of: a passiveillumination source, a laser illumination source, a broadband lightsource, an ambient light source, and combinations thereof. A thirddetector may be configured so as to detect the third plurality ofinteracted photon and generate a Raman data set representative of saidunknown material.

In one embodiment, at least one of a second detector and a thirddetector may comprise at least one focal plane array detector. Thisfocal plane array detector may comprise at least one of: an InGaAsdetector, a CMOS detector, an ICCD detector, a CCD detector, andcombinations thereof.

The system may further comprise a means for analyzing at least one ofsaid LIBS data set and said Raman data set to thereby identify saidunknown material. This means may comprise software configured to compareat least one of the LIBS data set and the Raman data set to a referencedata set. This may be achieved by applying at least one chemometrictechnique. This may also be achieved by applying a fusion algorithm.

In one embodiment, the system of the present disclosure may incorporateCONDOR-ST technology available from ChemImage Corporation, Pittsburgh,Pa. One embodiment of a system of the present disclosure is illustratedin FIG. 7. In one embodiment, the system 700 may comprise a firstoptical system optically coupled to an illumination source, which isillustrated in FIG. 7 as laser head 701. In addition to laser head 701the system may utilize at least one of: a broadband light source and anambient light source. In one embodiment, the laser head 710 may becoupled to a laser controller 719 for configuring the laser. In someembodiments, it may not be required for the illumination source to bephysically coupled to the system 700, for example when an ambient lightsource such as the sun is used as an illumination source. The firstoptical system may comprise a laser fiber coupler 702, a coupling optics703, and a telescope 704.

In one embodiment, the components of the first optical system arematched to one or more mirrors of the telescope, and expand the laserbeam to fill the mirror. The laser excitation pulse may propagate alongthe telescope's optical axis and present a laser spot that dills thetelescope's field of view at the chosen focal point. This allows for a180 degree backscattering collection geometry and enables repositioningand refocusing of the telescope 704 and laser spot simultaneously.

The system 700 may further comprise a visible imaging device, which isillustrated in FIG. 7 as a video capture device 705. The video capturedevice 705 may be configured to output a dynamic image of the region ofinterest and/or target area in real time. This video capture device 705may be configured to operate in a targeting mode in which it surveys aregion of interest/target area. Video is highly sensitive but may have alow specificity, in that it provides for a low level means ofclassifying objects based on morphological factors such a size, shape,and color. Such first-order discrimination may provide good guidance forhigher order classification and detection such as Raman, SWIR, and/orLIBS spectroscopy and imaging. In one embodiment, the video capturedevice 705 may utilize a target scope to provide for a large area ofview and zoom control. In one embodiment, this target scope may beincorporated into the lens 706 associated with the video capture device705. The system 700 may further comprise a narrow field video device 720for obtaining additional video data.

The video capture device 705 may use ambient light or light from laserlight source 701 to illuminate the target area. The video capture device705 may also collect a series of small images, that are recombined intoa larger, macro-image for analysis. The video capture device 705operates in the first order targeting mode to rapidly screen objectsbased on the intrinsic size, shape and color properties of theparticles. Regions of interest suspected to possess explosive residuesare located and identified, honing in on the target area at which toconduct further analysis using LIBS/Raman imaging spectroscopy thatprovide greater specificity.

The system 700 may also comprise a second optical system that collectsat least one of photons reflected, scattered, and/or plasma emitted by aregion of interest and/or target area. This second optical system maydirect the collected reflected photons to a first two-dimensional arrayof detection elements for SWIR spectroscopic analysis. This secondoptical system may direct the collected scattered and/or plasma emittedphotons to a fiber array spectral translator device. The second opticalsystem may comprise a telescope 704, a mirror 703, a filter 709, and acoupling optics 712. In one embodiment, the system may further comprisea dichroic beam splitter. In one embodiment, this dichroic beam splittermay enable simultaneous Raman acquisition and visual targeting.

Alternatively, a lens 709 can collect reflected photons from a region ofinterest and/or target area and direct the reflected photons to a SWIRfilter 710 which may comprise at least one of a SWIR liquid crystaltunable and SWIR multi-conjugate liquid crystal tunable filter. The SWIRfilter 710 may effectively filter a plurality of reflected photons intoa plurality of wavelength bands. The wavelength hands includewavelengths characteristic of the sample undergoing analysis. Thewavelengths that can be passed through a tunable filter may range from200 nm (ultraviolet) to 2000 nm (far infrared). The choice of a tunablefilter depends on the desired optical region and/or the nature of thesample being analyzed. The reflected photons may then be detected at aSWIR detector, shown in FIG. 7 as a SWIR camera 711. The SWIR camera 711may be configured to output a dynamic image of the region ofinterest/target area. The SWIR camera 711 may also be configured tooutput at least one of a SWIR hyperspectral image, a plurality ofspatially resolved SWIR spectra, and a plurality of spatially resolvedSWIR images. The SWIR camera 711 may be configured to operate inreal-time. The lens 709 may be configured to be operatively coupled totelescope optics to thereby increase the magnification and sensitivityof SWIR detection. Telescope optics may also he used to increaseillumination NA to decrease NOHD.

The second optical system's coupling optics 712 may be operativelycoupled to fiber array spectral translator device comprising a fiberarray spectral translator device fiber coupler 717 and fiber arrayspectral translator fiber optic bundles 714 a and 714 b. One end of saidfiber optic bundles 714 a and 714 b is operatively connected to at leastone spectrometer. In FIG. 7, fiber optic bundles 714 a and 714 b areoperatively connected to one of a Raman spectrometer 715 and a LIBSspectrometer 716. In another embodiment, a Raman grating array and aLIBS grating array may be incorporated into a single spectrometer.

A Raman spectrometer 715 may disperse said scattered photons output bysaid fiber array spectral translator device to generate a plurality ofspatially resolved Raman spectra. A Raman detector 717 may detect thespatially resolved Raman spectra. A LIBS spectrometer 716 may dispersesaid plasma emitted photons output by said fiber array spectraltranslator device to generate a plurality of spatially resolved atomicspectra. A LIBS detector may detect the spatially resolved atomicspectra.

The system 700 may also comprise a pan/tilt drive unit 726 and a focusdrive unit 725 to control the operation of elements of the system 700.The system 700 may further comprise a range finer 727 and a GPS sensor728 for finding, locating, and/or targeting. The system 700 may furthercomprise an operator control unit 724 for interfacing with a user andallowing the user to operate the system 700.

The system 700 may also comprise a cooling enclosure 727, a camera andtiming controller coupled to one or more detectors 717 and 718, and asystem computer 722. The system computer 722 may be configured toperform fusion and to control the system 700.

In another embodiment, the present disclosure provides for a storagemedium containing machine readable program code, which, when executed bya processor, causes said processor to perform the methods of the presentdisclosure. In one embodiment, processor may perform the following:illuminating a region of interest to thereby generate a first pluralityof interacted photons; filtering said first plurality of interactedphotons; detecting said first plurality of interacted photons to therebygenerate a test data set representative of said region of interest;analyzing said test data set to thereby identify a unknown sample withinsaid region of interest; illuminating a first portion of said unknownsample to thereby generate a second plurality of interacted photons;illuminating a second portion of said unknown sample to thereby generatea third plurality of interacted photons; filtering at least one of saidsecond plurality of interacted photons and said third plurality ofinteracted photons; detecting said second plurality of interactedphotons to thereby generate a LIBS data set representative of saidunknown sample; detecting said third plurality of interacted photons tothereby generate a Raman data set representative of said unknown sample;and analyzing at least one of said LIBS data set and said Raman data setto thereby identify said unknown sample.

In one embodiment, the storage medium, wherein said machine readableprogram code, when executed by said processor, causes said processor tofurther compare at least one of said LIBS data set and said Raman dataset to at least one reference data set associated with a known material.

The above description is not intended and should not be construed to belimited to the examples given but should be granted the full breadth ofprotection afforded by the appended claims and equivalents thereto.Although the disclosure is described using illustrative embodimentsprovided herein, it should be understood that the principles of thedisclosure are not limited thereto and may include modification theretoand permutations thereof.

What is claimed is:
 1. A method comprising: illuminating a region ofinterest to thereby generate a first plurality of interacted photons;filtering said first plurality of interacted photons; detecting saidfirst plurality of interacted photons to thereby generate a test dataset representative of said region of interest; analyzing said test dataset to thereby identify a unknown sample within said region of interest;illuminating a first portion of said unknown sample to thereby generatea second plurality of interacted photons; illuminating a second portionof said unknown sample to thereby generate a third plurality ofinteracted photons; filtering at least one of said second plurality ofinteracted photons and said third plurality of interacted photons;detecting said second plurality of interacted photons to therebygenerate a LIBS data set representative of said unknown sample;detecting said third plurality of interacted photons to thereby generatea Raman data set representative of said unknown sample; and analyzing atleast one of said LIBS data set and said Raman data set to therebyidentify said unknown sample.
 2. The method of claim 1 wherein said testdata set comprises a SWIR data set.
 3. The method of claim 2 whereinsaid SWIR data set comprises at least one hyperspectral SWIR image. 4.The method of claim 2 wherein said SWIR data set comprises at least oneof: a spatially accurate wavelength resolved SWIR image, a SWIRspectrum, and combinations thereof.
 5. The method of claim 1 whereinsaid test data set comprises a fluorescence data set.
 6. The method ofclaim 5 wherein said fluorescence data set comprises at least onehyperspectral fluorescence image.
 7. The method of claim 5 wherein saidfluorescence data set comprises at least one of: a spatially accuratewavelength resolved fluorescence image, a fluorescence spectrum, andcombinations thereof.
 8. The method of claim 1 wherein said firstportion of said unknown sample is illuminated using a first illuminationpattern.
 9. The method of claim 8 wherein said first illuminationpattern is selected from the group consisting of: a circle, a square, arectangle, an ellipse, an annulus, and combinations thereof.
 10. Themethod of claim 1 wherein said second portion of said unknown sample isilluminated using a second illumination pattern.
 11. The method of claim10 wherein said second illumination pattern comprises at least one of: acircle, a square, a rectangle, and ellipse, an annulus, and combinationsthereof.
 12. The method of claim 1 wherein said filtering of at leastone of said first plurality of interacted photons, said second pluralityof interacted photons, and said third plurality of interacted photons isachieved using at least one of: a tunable filter, a fixed filter, adielectric filter, and combinations thereof.
 13. The method of claim 12wherein said filtering is achieved using a tunable filter selected fromthe group consisting of: a liquid crystal tunable filter, amulti-conjugate tunable filter, an acousto-optical tunable filter, aLyot liquid crystal tunable filter, an Evans split-element liquidcrystal tunable filter, a Solc liquid crystal tunable filter, aferroelectric liquid crystal tunable filter, a Fabry Perot liquidcrystal tunable filter, and combinations thereof.
 14. The method ofclaim 1 wherein said analyzing of said LIBS data set and said Raman dataset further comprises applying at least one fusion algorithm to therebygenerate a fused data set representative of said unknown sample.
 15. Themethod of claim 14 further comprising analyzing said fused data set tothereby identify said unknown sample.
 16. The method of claim 1 whereinsaid LIBS data set comprises at least one hyperspectral LIBS image. 17.The method of claim 1 wherein said LIBS data set comprises at least oneof: a spatially accurate wavelength resolved LIBS image, a LIBSspectrum, and combinations thereof.
 18. The method of claim 1 whereinsaid Raman data set comprises at least one hyperspectral Raman image.19. The method of claim 1 wherein said Raman data set comprises at leastone of: a spatially accurate wavelength resolved Raman image, a Ramanspectrum, and combinations thereof.
 20. The method of claim 1 whereinsaid first portion of said unknown sample and said second portion ofsaid unknown sample are illuminated in one of the following modalities:sequentially, simultaneously, and combinations thereof.
 21. The methodof claim 1 wherein said first portion of said unknown sample and saidsecond portion of said unknown sample at least partially overlap. 22.The method of claim 1 wherein at least one of said first portion of saidunknown sample and said second portion of said unknown sample areilluminated using a laser illumination source.
 23. The method of claim 1wherein said region of interest is illuminated using at least one of: apassive illumination source, a laser light source, a broadband lightsource, an ambient light source, and combinations thereof.
 24. Themethod of claim 1 wherein at least one of said first portion of saidunknown sample and said second portion of said unknown sample isilluminated using at least one of: a passive illumination source, alaser light source, a broadband light source, an ambient light source.and combinations thereof.
 25. The method of claim 1 wherein said firstplurality of interacted photons, said second plurality of interactedphotons, and said third plurality of interacted photons are detectedusing a portable device.
 26. The method of claim 25 further wherein atleast one of said region of interest, said first portion of said unknownsample, said second portion of said unknown sample, and combinationsthereof are illuminated using said portable device.
 27. The method ofclaim 1 further comprising passing at least one of said first pluralityof interacted photons, said second plurality of interacted photons, saidthird plurality of interacted photons, and combinations thereof througha fiber array spectral translator device.
 28. The method of claim 1wherein at least one of said first plurality of interacted photons, saidsecond plurality of interacted photons, said third plurality ofinteracted photons, and combinations thereof, are detected using a focalplane array detector.
 29. The method of claim 28 wherein said focalplane array detector is selected from the group consisting of: an InGaAsdetector, a CMOS detector, an ICCD detector, a CCD detector, andcombinations thereof.
 30. The method of claim 1 wherein said analyzingof said test data set further comprises comparing said test data set toat least one reference data set using a chemometric technique.
 31. Themethod of claim 1 wherein said analyzing of at least one of said LIBSdata set and said Raman data set further comprises comparing at leastone of said LIBS data set and said Raman data set to at least onereference data set using a chemometric technique.
 32. The method ofclaim 1 wherein identifying said unknown sample further comprisesidentifying said unknown sample as at least one of: a biologicalmaterial, a chemical material, an explosive material, a hazardousmaterial, a concealment material, a non-hazardous material, andcombinations thereof.